Poped examples tp

R/writeLines.R

@@ -0,0 +1,52 @@
+# Function to write tree scructure for folders
+
+# Install fs package if not already installed
+if (!requireNamespace("fs", quietly = TRUE)) {
+      install.packages("fs")
+}
+
+# Load the fs package
+
+
+# Define a function to get and save the folder tree with symbols
+save_folder_tree_with_symbols <- function(path = ".", output_file = "folder_tree.txt") {
+      library(fs)
+      # Use fs::dir_tree to create the folder tree and get individual lines
+      folder_tree <- dir_tree(path, recurse = TRUE)
+
+      # Define a function to add symbols based on depth
+      add_symbols <- function(lines) {
+            result <- c()
+            depth <- 0
+
+            for (i in seq_along(lines)) {
+                  # Calculate the depth based on the number of slashes in the path
+                  depth <- sum(strsplit(lines[i], "/")[[1]] != "")
+
+                  # Determine if it's a file or folder and if it's the last in its group
+                  is_last <- (i == length(lines)) || 
+                        (sum(strsplit(lines[i + 1], "/")[[1]] != "") <= depth)
+
+                  # Add appropriate symbols for the depth level
+                  prefix <- ifelse(depth > 1, paste0(strrep("│  ", depth - 2), ifelse(is_last, "└─ ", "├─ ")), "")
+
+                  result <- c(result, paste0(prefix, basename(lines[i])))
+            }
+            return(result)
+      }
+
+      # Create a visual representation of the folder tree with symbols
+      visual_tree <- add_symbols(folder_tree)
+
+      # Print the visual folder tree to the console
+      cat(visual_tree, sep = "\n")
+
+      # Save the visual folder tree to a text file
+      writeLines(visual_tree, output_file)
+}
+
+# Call the function on the
+
+save_folder_tree_with_symbols(path = here::here("SCRIPTS"), output_file = "folder_tree_SCRIPTS.txt")
+save_folder_tree_with_symbols(path = here::here("ANALYSES"), output_file = "folder_tree_ANALYSES.txt")
+save_folder_tree_with_symbols(path = here::here("DATASETS"), output_file = "folder_tree_DATASETS.txt")

inst/poped/PopED_output_summary_D_cont_opt_1.txt

@@ -0,0 +1,229 @@
+PopED Results 
+
+        2024-10-24 00:44:14.791373
+
+==============================================================================
+Model description : PopED model 
+
+Model Sizes : 
+Number of individual model parameters                  g[j]    : Ng    = 10
+Number of population model fixed parameters            bpop[j] : Nbpop = 8
+Number of population model random effects parameters   b[j]    : Nb    = 8
+
+Typical Population Parameters:
+bpop[1]: 3.908 
+bpop[2]: -2.188 
+bpop[3]: 0.558 
+bpop[4]: -0.1864 
+bpop[5]: 2.261 
+bpop[6]: 0.2105 
+bpop[7]: 3.708 
+bpop[8]: -0.7089 
+
+Between Subject Variability matrix D (variance units) 
+0.0625 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
+0.0000 0.0625 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
+0.0000 0.0000 0.0625 0.0000 0.0000 0.0000 0.0000 0.0000
+0.0000 0.0000 0.0000 0.0625 0.0000 0.0000 0.0000 0.0000
+0.0000 0.0000 0.0000 0.0000 0.0625 0.0000 0.0000 0.0000
+0.0000 0.0000 0.0000 0.0000 0.0000 0.0625 0.0000 0.0000
+0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0625 0.0000
+0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0625
+
+Diagonal Elements of D [sqrt(param)]:
+D[1,1]: 0.0625 [ 0.25] 
+D[2,2]: 0.0625 [ 0.25] 
+D[3,3]: 0.0625 [ 0.25] 
+D[4,4]: 0.0625 [ 0.25] 
+D[5,5]: 0.0625 [ 0.25] 
+D[6,6]: 0.0625 [ 0.25] 
+D[7,7]: 0.0625 [ 0.25] 
+D[8,8]: 0.0625 [ 0.25] 
+
+Residual Unexplained Variability matrix SIGMA (variance units) : 
+0.00927944 0.00000000 0.00000000 0.00000000
+0.00000000 0.00100000 0.00000000 0.00000000
+0.00000000 0.00000000 0.02246920 0.00000000
+0.00000000 0.00000000 0.00000000 0.00100000
+
+Diagonal Elements of SIGMA [sqrt(param)]:
+SIGMA[1,1]: 0.009279 [0.09633] 
+SIGMA[2,2]: 0.001 [0.03162] 
+SIGMA[3,3]: 0.02247 [0.1499] 
+SIGMA[4,4]: 0.001 [0.03162] 
+
+==============================================================================
+Experiment description (design and design space)
+
+Number of individuals: 100
+Number of groups (individuals with same design): 2
+Number of individuals per group:
+     Group 1: 50
+     Group 2: 50
+Number of samples per group:
+ Number of discrete experimental variables: 0
+Number of model covariates: 2
+
+Initial Sampling Schedule
+Group 1: Model 1:   0.02   0.25      1      3     10
+Group 1: Model 2:   0.02   0.25      1      3     10
+Group 2: Model 1:      1      7     15     28     42
+Group 2: Model 2:      1      7     15     28     42
+
+Minimum allowed sampling values
+Group 1: Model 1:   0.02   0.25      1      3     10
+Group 1: Model 2:   0.02   0.25      1      3     10
+Group 2: Model 1:      1      7     15     28     42
+Group 2: Model 2:      1      7     15     28     42
+
+Maximum allowed sampling values
+Group 1: Model 1:   0.02   0.25      1      3     10
+Group 1: Model 2:   0.02   0.25      1      3     10
+Group 2: Model 1:      1      7     15     28     42
+Group 2: Model 2:      1      7     15     28     42
+
+Covariates:
+Group 1: 1 : 300
+Group 2: 2 : 10000
+
+===============================================================================
+Initial design evaluation
+
+Initial OFV = 138.56
+
+Efficiency criterion [usually defined as OFV^(1/npar)]  = 2203.41
+
+Initial design
+expected relative standard error
+(%RSE, rounded to nearest integer)
+      Parameter    Values   RSE_0
+            tvc      3.91       1
+           tk10     -2.19       2
+           tk12     0.558       8
+           tk21    -0.186      24
+            tvm      2.26       2
+           tkmc      0.21      48
+           tk03      3.71       1
+           tk30    -0.709       7
+       d_eta.vc    0.0625      17
+      d_eta.k10    0.0625      32
+      d_eta.k12    0.0625      28
+      d_eta.k21    0.0625      32
+       d_eta.vm    0.0625      25
+      d_eta.kmc    0.0625     102
+      d_eta.k03    0.0625      21
+      d_eta.k30    0.0625      32
+   sig_var_eps1   0.00928      11
+   sig_var_eps3    0.0225      18
+
+==============================================================================
+Criterion Specification
+
+OFV calculation for FIM: 4 
+  1=Determinant of FIM,
+  4=log determinant of FIM,
+  6=determinant of interesting part of FIM (Ds)
+
+Approximation method: 0
+  0=FO, 
+  1=FOCE, 
+  2=FOCEI, 
+  3=FOI
+
+Fisher Information Matrix type: 1
+  0=Full FIM,
+  1=Reduced FIM,
+  2=weighted models,
+  3=Loc models,
+  4=reduced FIM with derivative of SD of sigma as pfim,
+  5=FULL FIM parameterized with A,B,C matrices & derivative of variance,
+  6=Calculate one model switch at a time, good for large matrices,
+  7=Reduced FIM parameterized with A,B,C matrices & derivative of variance
+
+Design family: 1
+  D-family design (1) or 
+  ED-family design (0) 
+  (with or without parameter uncertainty)
+
+==============================================================================
+Optimization of design parameters
+
+* Optimize Sampling Schedule
+
+*******************************
+Initial Value
+ OFV(mf) = 138.56
+*******************************
+
+RS - It. : 5   OFV : 138.56
+RS - It. : 10   OFV : 138.56
+RS - It. : 15   OFV : 138.56
+RS - It. : 20   OFV : 138.56
+RS - It. : 25   OFV : 138.56
+RS - It. : 30   OFV : 138.56
+RS - It. : 35   OFV : 138.56
+RS - It. : 40   OFV : 138.56
+RS - It. : 45   OFV : 138.56
+RS - It. : 50   OFV : 138.56
+RS - It. : 55   OFV : 138.56
+RS - It. : 60   OFV : 138.56
+RS - It. : 65   OFV : 138.56
+RS - It. : 70   OFV : 138.56
+RS - It. : 75   OFV : 138.56
+RS - It. : 80   OFV : 138.56
+RS - It. : 85   OFV : 138.56
+RS - It. : 90   OFV : 138.56
+RS - It. : 95   OFV : 138.56
+RS - It. : 100   OFV : 138.56
+RS - It. : 105   OFV : 138.56
+RS - It. : 110   OFV : 138.56
+RS - It. : 115   OFV : 138.56
+RS - It. : 120   OFV : 138.56
+RS - It. : 125   OFV : 138.56
+RS - It. : 130   OFV : 138.56
+RS - It. : 135   OFV : 138.56
+RS - It. : 140   OFV : 138.56
+RS - It. : 145   OFV : 138.56
+RS - It. : 150   OFV : 138.56
+RS - It. : 155   OFV : 138.56
+RS - It. : 160   OFV : 138.56
+RS - It. : 165   OFV : 138.56
+RS - It. : 170   OFV : 138.56
+RS - It. : 175   OFV : 138.56
+RS - It. : 180   OFV : 138.56
+RS - It. : 185   OFV : 138.56
+RS - It. : 190   OFV : 138.56
+RS - It. : 195   OFV : 138.56
+RS - It. : 200   OFV : 138.56
+RS - It. : 205   OFV : 138.56
+RS - It. : 210   OFV : 138.56
+RS - It. : 215   OFV : 138.56
+RS - It. : 220   OFV : 138.56
+RS - It. : 225   OFV : 138.56
+RS - It. : 230   OFV : 138.56
+RS - It. : 235   OFV : 138.56
+RS - It. : 240   OFV : 138.56
+RS - It. : 245   OFV : 138.56
+RS - It. : 250   OFV : 138.56
+RS - It. : 255   OFV : 138.56
+RS - It. : 260   OFV : 138.56
+RS - It. : 265   OFV : 138.56
+RS - It. : 270   OFV : 138.56
+RS - It. : 275   OFV : 138.56
+RS - It. : 280   OFV : 138.56
+RS - It. : 285   OFV : 138.56
+RS - It. : 290   OFV : 138.56
+RS - It. : 295   OFV : 138.56
+RS - It. : 300   OFV : 138.56
+
+*******************************
+RS Results
+ OFV(mf) = 138.56
+
+Optimized Sampling Schedule
+Group 1: Model 1:   0.02   0.25      1      3     10
+Group 1: Model 2:   0.02   0.25      1      3     10
+Group 2: Model 1:      1      7     15     28     42
+Group 2: Model 2:      1      7     15     28     42
+*********************************
+

inst/poped/ex.1.a.PK.1.comp.oral.md.intro.babelmixr2.R

@@ -35,15 +35,14 @@ f <- function() {
   })
 }
 
-# minxt, maxxt
 e <- et(list(c(0, 10),
              c(0, 10),
              c(0, 10),
              c(240, 248),
              c(240, 248))) %>%
   as.data.frame()
 
-#xt
+# PopED xt equivalent
 e$time <-  c(1,2,8,240,245)
 
 
@@ -69,7 +68,6 @@ plot_model_prediction(babel.db, IPRED=T, DV=T, separate.groups=T, model_num_poin
 
 evaluate_design(babel.db)
 
-
 ## original: > shrinkage(poped.db)
 ## # A tibble: 9 × 5
 ##     d_V  d_KA   d_CL type       group
@@ -86,40 +84,49 @@ evaluate_design(babel.db)
 shrinkage(babel.db)
 
 # Optimization of sample times
-
-output <- poped_optim(babel.db, opt_xt =TRUE)
+# Note: The parallel option does not work well with Windows machines at this moment. 
+# Please set parallel = FALSE if you are working on a Windows machine
+output <- poped_optim(babel.db, opt_xt =TRUE, parallel=TRUE)
 
 # Evaluate optimization results
 summary(output)
 
+## From original
+# V        KA        CL       d_V      d_KA      d_CL 
+# 6.281944  7.726279  4.295908 32.416232 49.062880 26.363021 
 get_rse(output$FIM,output$poped.db)
 
 plot_model_prediction(output$poped.db)
 
-
 # Optimization of sample times and doses
-output_2 <- poped_optim(output$poped.db, opt_xt =TRUE, opt_a = TRUE)
+# Note: The parallel option does not work well with Windows machines at this moment. 
+# Please set parallel = FALSE if you are working on a Windows machine
+output_2 <- poped_optim(output$poped.db, opt_xt =TRUE, opt_a = TRUE, parallel = TRUE)
 
 summary(output_2)
 
+# From original
+# V        KA        CL       d_V      d_KA      d_CL 
+# 6.252332  7.547072  4.240929 32.205996 47.014629 25.684326 
 get_rse(output_2$FIM,output_2$poped.db)
 
 plot_model_prediction(output_2$poped.db)
 
-
 # Optimization of sample times with only integer time points in design space
 # faster than continuous optimization in this case
 babel.db.discrete <- create.poped.database(babel.db,discrete_xt = list(0:248))
 
-output_discrete <- poped_optim(babel.db.discrete, opt_xt=T)
-
+# Note: The parallel option does not work well with Windows machines at this moment. 
+# Please set parallel = FALSE if you are working on a Windows machine
+output_discrete <- poped_optim(babel.db.discrete, opt_xt=T, parallel = TRUE)
 
 summary(output_discrete)
 
+# V        KA        CL       d_V      d_KA      d_CL 
+# 6.331614  8.009220  4.297905 32.351741 51.795028 26.386514 
 get_rse(output_discrete$FIM,output_discrete$poped.db)
 
 plot_model_prediction(output_discrete$poped.db)
 
-
 # Efficiency of sampling windows
 plot_efficiency_of_windows(output_discrete$poped.db, xt_windows=1)